Tri-level Hierarchical Coordinated Control of a Largescale Electric Vehicle Charging in the Smart Grid
Doctor of Philosophy (PhD)
Electrical and Computer Engineering
First Advisor's Name
Dr. Osama Mohammed
First Advisor's Committee Title
Second Advisor's Name
Dr. Xia Jin
Second Advisor's Committee Title
Third Advisor's Name
Dr. Ahmed Ibrahim
Third Advisor's Committee Title
Fourth Advisor's Name
Dr. Ahmed Elsayed
Fourth Advisor's Committee Title
Fifth Advisor's Name
Dr. Stavros Georgakopoulos
Fifth Advisor's Committee Title
electrical and electronics, power and energy
Date of Defense
Electric Vehicles (EVs) are considered one of humanity's greatest hopes to combat the climate change crises in light of their great potentials to reduce Greenhouse Gases (GHG) emissions from two main sources: the electric power industry and the fossil-based transportation sector. To help expedite the large-scale adoption of EVs on the roads, optimal solutions are needed to overcome the technical and operational barriers that face the electrical network. This is due to the introduction of significant load levels from EVs; a substantial number is expected during peak demand hours. This dissertation addresses the various interaction between different parts of the electrical system in a hierarchical optimization framework to ensure proper large-scale integration of electric vehicles; without harm to the grid or the user. To achieve our goals of achieving optimum operation scenarios, we developed a tri-level centralized and decentralized optimization methodologies with smart coordination algorithms. This will ensure optimal decisions with the simplest required communication infrastructure. Specifically, information from the EVs’ owners are collected by an aggregator located at the charging station. In a timely fashion, the aggregator sends the most updated scheduling information to its assigned microgrid that ensures no violation occurs within its jurisdiction and establishes a pricing signal for each aggregator. The microgrid takes a decision based on the downstream input information from other viii aggregators attached to it and upstream input information from a system operator that provides additional energy if needed and update the microgrids based on the overall grid’s operation. Additionally, we developed a two-stage optimization strategy to ensure proper EVs charging and discharging coordination considering voltage and reactive power control levels. The optimization strategy starts with the decomposition of the power distribution network into optimal partitions based on their voltage sensitivity levels, then solves a centralized energy coordination problem using mixed-integer linear programming. The optimization problem takes into consideration various aspects of the systems’ operation that include reactive power compensation devices and active power curtailment of PV inverters. The developed solutions presented in this dissertations have been verified and tested experimentally.
Aljohani, Tawfiq M., "Tri-level Hierarchical Coordinated Control of a Largescale Electric Vehicle Charging in the Smart Grid" (2021). FIU Electronic Theses and Dissertations. 4619.
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